Segmenter Progress

I’ve continued to improve my segmenter. Here in the above, I’ve used it to detect people in images that aren’t part of the training set, and I’ve used the output to then redact the people from the images. These images are free for use from My segmenter is best at “chest and up” pictures of people, but it’s been trained on various group photos and various poses. This segmenter does not use query images, and instead it operates in line. A 256 x 256 x 3 channel zero (black) padded image is input, and the output is a 256 x 256 x 3 image where the pixels that are “part of people” are white, and the pixels that are “not people” are black. That output image can then be used with lots of different types of post processing to do things such as I’ve shown above where the people are redacted. The segmenter model consists of two sub models: An encoder with 8.37 million parameters and a decoder with 0.63 million parameters.

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